Three-dimensional modeling of weed plants using low-cost photogrammetry

Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expand...

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Detalles Bibliográficos
Autores: Andújar, Dionisio, Calle, Mikel, Fernández-Quintanilla, César, Ribeiro Seijas, Ángela, Dorado, José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/170692
Acceso en línea:http://hdl.handle.net/10261/170692
Access Level:acceso abierto
Palabra clave:digital surface models
structure from motion
plant phenotyping
multi-view stereo
RGB imagery
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spelling Three-dimensional modeling of weed plants using low-cost photogrammetryAndújar, DionisioCalle, MikelFernández-Quintanilla, CésarRibeiro Seijas, ÁngelaDorado, Josédigital surface modelsstructure from motionplant phenotypingmulti-view stereoRGB imagerySensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches.his research was funded by the projects AGL2017-83325-C4-1-R and AGL2017-83325-C4-3-R (Spanish Ministry of Economy and Competition) and by the RYC-2016-20355 agreement.Peer ReviewedMolecular Diversity Preservation InternationalMinisterio de Economía, Industria y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2018201820182018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/170692reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#MINECO/AGL2017/83325-C4-1-RMINECO/RYC-2016/20355Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1706922026-05-22T06:33:51Z
dc.title.none.fl_str_mv Three-dimensional modeling of weed plants using low-cost photogrammetry
title Three-dimensional modeling of weed plants using low-cost photogrammetry
spellingShingle Three-dimensional modeling of weed plants using low-cost photogrammetry
Andújar, Dionisio
digital surface models
structure from motion
plant phenotyping
multi-view stereo
RGB imagery
title_short Three-dimensional modeling of weed plants using low-cost photogrammetry
title_full Three-dimensional modeling of weed plants using low-cost photogrammetry
title_fullStr Three-dimensional modeling of weed plants using low-cost photogrammetry
title_full_unstemmed Three-dimensional modeling of weed plants using low-cost photogrammetry
title_sort Three-dimensional modeling of weed plants using low-cost photogrammetry
dc.creator.none.fl_str_mv Andújar, Dionisio
Calle, Mikel
Fernández-Quintanilla, César
Ribeiro Seijas, Ángela
Dorado, José
author Andújar, Dionisio
author_facet Andújar, Dionisio
Calle, Mikel
Fernández-Quintanilla, César
Ribeiro Seijas, Ángela
Dorado, José
author_role author
author2 Calle, Mikel
Fernández-Quintanilla, César
Ribeiro Seijas, Ángela
Dorado, José
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía, Industria y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv digital surface models
structure from motion
plant phenotyping
multi-view stereo
RGB imagery
topic digital surface models
structure from motion
plant phenotyping
multi-view stereo
RGB imagery
description Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/170692
url http://hdl.handle.net/10261/170692
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
MINECO/AGL2017/83325-C4-1-R
MINECO/RYC-2016/20355
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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